In this study, we train and evaluate the performance of VRLS in simulative environments. Yet, the proposed methods might as well be implemented in a real-world vehicular network. In a real-world scenario, network vendors or operators would implement VRLS as an intelligent controller deployed at the edge of the network and integrated into the radio access network (RAN), thanks to the enabling architecture envisioned for 5G and beyond networks [1]}. Within this architecture, the BSs deployed at the entrance/exit of the DOCA can be realized as remote radio units. While these radio units serve physical layer functions, they are connected to a centralized entity that is responsible for resource allocation and other higher-layer functionalities, where VRLS can be implemented. By implementing VRLS, operators would aim at ensuring seamless quality of V2V communications when vehicular users experience coverage losses. This would in turn ensure safer and more efficient road traffic. With regards to deployment and operation costs of VRLS, since being a logical entity, it can be implemented as software and can make use of the processing hardware available at the network infrastructure. Additional processing power is necessary to train the RL agent (with high processing requirements and the possible need to pre-train on the simulated environments), and to operate it (with relatively low processing requirements). VRLS can again benefit from the fact that in 5G RAN, there are more computational resources deployed at the network edge to train learning algorithms [1]}.
